A year in R

Tis the season for retrospectives, and I happened across a particularly appropriate one for statistical programming - R in 2010.

In a nutshell, R is an open-source object-oriented functional programming language intended for statistical applications and based on S (a product from Bell Labs in the 70s). Over the past decade it has become the de facto standard for statisticians (e.g. people actually in stats departments), as well as enjoyed increasing use in biostatistics, computer science, and the social sciences.

The most interesting part of the linked post is the first section listing the top 14 R-related posts from 2010. This gives a good idea of the range of tasks R can be used for, from publishing quality graphics to data mining to game theory (Prisoner's dilemma).

Personally I've most used R in combination with Sweave/LaTeX ("literate programming") to simultaneously perform statistical analysis and typeset the report (potentially even automating the process). This guide looks interesting, though it adds Eclipse to the mix (I find Emacs Speaks Statistics to be the best way to use R).

Of course if all of the above sounds like gibberish to you and you just want to get started, there's a bunch of resources for that too. R is a powerful, well-supported, and surprisingly expressive language and I would advocate for anybody considering statistics beyond basic summaries (e.g. means and the like). It has a steep learning curve (the interactive prompt "command line") but can be made gentler by installing GUI plugins such as R Commander.

All in all, a good year for a good software package.